Learn what is correlation. You will also what is regression in the next video
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short introduction on Association Rule with definition & Example, are explained.
Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database.
Parts of Association rule is explained with 2 measurements support and confidence.
types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples.
Names of Association rule algorithm and fields where association rule is used is also mentioned.

Learn about correlation analysis in this video
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Basic Statistical Tests
Training session with Dr Helen Brown, Senior Statistician, at The Roslin Institute, December 2015.
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These training sessions were given to staff and research students at the Roslin Institute. The material is also used for the Animal Biosciences MSc course taught at the Institute.
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*Recommended YouTube playback settings for the best viewing experience: 1080p HD
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Content:
Measuring association
How associated are two measurements?
Correlation coefficient calculated from raw data values
--The more values deviate from a perfect line, the lower the correlation
Most easily calculated within a package
Sometimes referred to as ’Pearson’s correlation coefficient’
Correlation between platelets and WBC in new born calves
Correlation for non-normal data
-Standard correlation coefficient (r) is less appropriate for non-normal data and is particularly sensitive to outlying values
-An alternative Spearman rank correlation coefficient is calculated based on the ranks of the data

An explanation of how to compute the chi-squared statistic for independent measures of nominal data.
For an explanation of significance testing in general, see http://evc-cit.info/psych018/hyptest/index.html
There is also a chi-squared calculator at http://evc-cit.info/psych018/chisquared/index.html

Part three of our introduction to similarity and dissimilarity, we discuss correlation and visually evaluating it.
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In this video, I discussed chi square test with the example for correlation analysis (Nominal Data) in data mining.
A correlation relationship between two attributes can be discovered by X2 (chi-square) test.

Correlation using scattered diagram and KARL PARSON method is explained in this video along with example.
This video include the detailed concept of solving any kind of problem related to correlation.
Basically correlation refers to a statistical technique which we use to find out the relation exist between two or more variables.
I hope this video will help you to solve any kind of problem related to Correlation.
Thanks.
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In this video you will learn how to measure the strength of relation between variables by calculating correlation and interpreting it.
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In this text analytics with R video I've talked abou how you can find correlation between. words and understand the context behind the entire text and the motive of speaker or writer. This helps understand how one specific important word is related to other words in the entire text and we can limit the correlation also to look at only those words which has either high or low correlation.
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In this Video Professor Drake explains the Lift calculation when doing market basket analysis. Lift tells you how much better than chance item x will appear in the cart if you already know that item Y is in the cart.

Association Mining with example in Hindi | DWM || Data Mining | Dataware house and Mining
All topics of Dataware House And Mining (DWM) will be covered in these series of videos.
All videos here are for all students and teachers form beginner to expert level.
All subjects solution are explained here in easy and simple way.
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Welcome to the course Basics of Statistics - A Comprehensive Study.
Statistics is required in every walk of business life to take decisions. When we take decision, it should be an informed one. Once we have statistics, we can be sure that decision is taken considering various factors. In this course, you will learn about the basics of statistics in depth covering
a) Introduction to Statistics;
b) Collection of Data;
c) Presentation of Data;
d) Frequency Distribution;
e) Measures of Central Tendency covering Arithmetic Mean, Median and Mode
f) Measures of Dispersion covering Range, Quartile Deviation and Standard Deviation
g) Correlation
h) Regression.
This course is basically a bundle of other courses namely
i) Basics of Business Statistics
ii) Statistics - Measures of Central Tendency
iii) Statistics - Measures of Dispersion
iv) Statistics and Correlation.
If you are buying this course, make sure you don't buy the above courses. This course is structured in self paced learning style. Video lectures are used for delivering the course content. Numerous case studies were solved in hand written presentation.
Take this course to gain good knowledge in basics of statistics.
What are the requirements?
• Students can approach this course with fresh mind.
• No prior knowledge in Statistics is required.
What am I going to get from this course?
• Over 80 lectures and 6.5 hours of content!
• Understand Basics of Statistics
• Understand Mean, Median and Mode
• Understand Deviations like Quartile Deviation, Standard Deviation, etc.
• Understand Correlation
• Understand Reggression
What is the target audience?
• CA / CMA / CS Students
• Students pursuing CA / CMA / CS / Higher Secondary / Statistics courses
• B.Com I Year Students

Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation.
0:00 Introduction to bivariate correlation
2:20 Why does SPSS provide more than one measure for correlation?
3:26 Example 1: Pearson correlation
7:54 Example 2: Spearman (rhp), Kendall's tau-b
15:26 Example 3: correlation matrix
I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation.
Watch correlation and regression: https://youtu.be/tDxeR6JT6nM
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Correlation of 2 rodinal variables, non monotonic
This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative.
Good luck

Learn the basics of Machine Learning with R. Start our Machine Learning Course for free: https://www.datacamp.com/courses/introduction-to-machine-learning-with-R
First up is Classification. A *classification problem* involves predicting whether a given observation belongs to one of two or more categories. The simplest case of classification is called binary classification. It has to decide between two categories, or classes. Remember how I compared machine learning to the estimation of a function? Well, based on earlier observations of how the input maps to the output, classification tries to estimate a classifier that can generate an output for an arbitrary input, the observations. We say that the classifier labels an unseen example with a class.
The possible applications of classification are very broad. For example, after a set of clinical examinations that relate vital signals to a disease, you could predict whether a new patient with an unseen set of vital signals suffers that disease and needs further treatment. Another totally different example is classifying a set of animal images into cats, dogs and horses, given that you have trained your model on a bunch of images for which you know what animal they depict. Can you think of a possible classification problem yourself?
What's important here is that first off, the output is qualitative, and second, that the classes to which new observations can belong, are known beforehand. In the first example I mentioned, the classes are "sick" and "not sick". In the second examples, the classes are "cat", "dog" and "horse". In chapter 3 we will do a deeper analysis of classification and you'll get to work with some fancy classifiers!
Moving on ... A **Regression problem** is a kind of Machine Learning problem that tries to predict a continuous or quantitative value for an input, based on previous information. The input variables, are called the predictors and the output the response.
In some sense, regression is pretty similar to classification. You're also trying to estimate a function that maps input to output based on earlier observations, but this time you're trying to estimate an actual value, not just the class of an observation.
Do you remember the example from last video, there we had a dataset on a group of people's height and weight. A valid question could be: is there a linear relationship between these two? That is, will a change in height correlate linearly with a change in weight, if so can you describe it and if we know the weight, can you predict the height of a new person given their weight ? These questions can be answered with linear regression!
Together, \beta_0 and \beta_1 are known as the model coefficients or parameters. As soon as you know the coefficients beta 0 and beta 1 the function is able to convert any new input to output. This means that solving your machine learning problem is actually finding good values for beta 0 and beta 1. These are estimated based on previous input to output observations. I will not go into details on how to compute these coefficients, the function `lm()` does this for you in R.
Now, I hear you asking: what can regression be useful for apart from some silly weight and height problems? Well, there are many different applications of regression, going from modeling credit scores based on past payements, finding the trend in your youtube subscriptions over time, or even estimating your chances of landing a job at your favorite company based on your college grades.
All these problems have two things in common. First off, the response, or the thing you're trying to predict, is always quantitative. Second, you will always need input knowledge of previous input-output observations, in order to build your model. The fourth chapter of this course will be devoted to a more comprehensive overview of regression.
Soooo.. Classification: check. Regression: check. Last but not least, there is clustering. In clustering, you're trying to group objects that are similar, while making sure the clusters themselves are dissimilar.
You can think of it as classification, but without saying to which classes the observations have to belong or how many classes there are.
Take the animal photo's for example. In the case of classification, you had information about the actual animals that were depicted. In the case of clustering, you don't know what animals are depicted, you would simply get a set of pictures. The clustering algorithm then simply groups similar photos in clusters.
You could say that clustering is different in the sense that you don't need any knowledge about the labels. Moreover, there is no right or wrong in clustering. Different clusterings can reveal different and useful information about your objects. This makes it quite different from both classification and regression, where there always is a notion of prior expectation or knowledge of the result.

This video will show you how to make a simple scatter plot. Remember to put your independent variable along the x-axis, and you dependent variable along the y-axis. For more videos please visit http://www.mysecretmathtutor.com

The Tron Roadmap.
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With an already existing user base of over 180 million, the opportunities for this Blockchain and cryptocurrency seem enormous. Also, it will likely not have to bootstrap and this is a deviation from the trajectory of most apps and platforms of this nature.
An outsider continues to steal the crypto spotlight.
Investors started telling CoinDesk in late December that Telegram was looking at doing some kind of ICO.
All that on top of promising super fast payments and micropayments using mobile devices, with negligible transaction fees.
With these announcements, fake sites quickly popped up claiming to be the place to buy grams. Confirming that one was fake in a tweet proved to be the closest Durov has come to a public confirmation of the crowdsale.

By mid-month, the idea that Telegram might raise its fundraising round even higher was reported by Bloomberg.
Early February.
They come up with a lockup period that releases tokens after four waiting periods, the longest one last 18 months.
Late February.
Finally, Telegram has apparently offered investors some kind of refund provision if it fails to deliver the TON platform by the end of October 2019, Business Insider reported.
The leader in blockchain news, CoinDesk is a media outlet that strives for the highest journalistic standards and abides by a strict set of editorial policies. CoinDesk is an independent operating subsidiary of Digital Currency Group, which invests in cryptocurrencies and blockchain startups.
The filing names Ton Issuer Inc. and Telegram Group Inc. along with the two individuals, Pavel Durov and Nikolai Durov, as related persons.
Apart from building on the extensive userbase Telegram has amassed, and serving as a medium of exchange with a native cryptocurrency called GRAM, the TON platform also aims to include smart contracts and decentralized services such as TON Storage and TON Proxy.
Leverage and Margin Explained.